Breast cancer is a heterogeneous disease with variable biology and often-unpredictable clinical outcomes. Genomic technologies have shown promise to better discriminate between patients of similar clinicopathologic stage and in some cases predict clinical outcomes. We and others have published studies of array-based predictive modeling of breast cancer outcomes, including ER status, lymph node status, and disease recurrence. While these initial studies raise hope for genomic-based personalized outcome predictions, significant questions must be answered before expression array-based prognostics can be incorporated into clinical medicine. For example, does normal variation in breast tumor gene expression affect expression-based outcome predictions? Our recent data suggest that replicate samples of similar tumor taken from the same breast tumor at two different times show consistent patterns of global gene expression. On the other hand, replicate tumor specimens of different cellular heterogeneity or those subjected to different periods of ischemia have significant differences in global gene expression as well. Importantly, this variation, which can reasonably be expected to occur under routine clinical conditions, gives different model predictions of outcome in some cases. We now believe that understanding variation in gene expression and incorporating knowledge about this variation in genomic-based models may provide useful information to improve the accuracy of these models. In this proposal, we will test this hypothesis that ascertainment of biologic and process variability in breast cancer gene expression will improve the ability of gene expression-based models to predict breast cancer outcomes, including ER status, metastasis to axillary lymph nodes and disease recurrence. Specifically, we aim to: Specific Aim 1: To determine whether gene expression data from core biopsy specimens of different cellular content give similar predictions in a gene expression-based predictive model of ER status, axillary lymph node metastasis and recurrence in breast cancer. Specific Aim 2: To identify changes in breast tumor gene expression with tissue ischemia and determine the impact of these changes on gene expression-based predictive models of ER status, axillary lymph node status and recurrence in breast cancer. Specific Aim 3: To measure variability in gene expression in T1N0M0 hormone receptor positive breast cancer in pre- and postmenopausal women and ascertain whether this variability changes expression-based model predictions of ER status, axillary lymph node status and recurrence in breast cancer.